Bibliographic record
Abstract
This mini series emerges from Amanda’s doctoral research with climate justice organizers in Canada. Cite as: Harvey-Sánchez, A. & DaSilva, J. (2023). “Divestment Generation Mini Series, Third Generation (Ep.3)”. Climate Justice Toronto. EPISODE RESOURCES: - UofT Fossil Fuel Divestment Timeline: https://tinyurl.com/2pz26m4u - Divestment and Beyond Magazine Article (by Amanda Harvey-Sánchez & Sydney Lang): https://briarpatchmagazine.com/articles/view/divestment-and-beyond - Discovering University Worlds: https://tinyurl.com/y3px3z6u - This Changes Everything (by Naomi Klein): https://www.simonandschuster.com/books/This-Changes-Everything/Naomi-Klein/9781451697391 - The Leap Manifesto: https://leapmanifesto.org/en/the-leap-manifesto/ SONG: Adaptation of “Which Side Are You On?” by Pete Seeger; https://www.youtube.com/watch?v=bsNVzwuJeVk&ab_channel=PeteSeeger-Topic LYRICS: Does it weigh on you at all? [High] Does it weigh on you at all? [Low] (x 2) Corporations raised you up but we can make you fall They picked a war with all of us does it weigh on you at all? SOCIAL MEDIA & CONTACT INFO: Amanda Harvey-Sánchez: @amanda_hsanchez; Julia DaSilva: julia.dasilva713@gmail.com; Climate Justice Toronto: @climatejusticeto, @CJusticeTO; Climate Justice UofT (Formerly Leap UofT): @climatejusticeuoft, @cjuoft; 2185 Art Collective: @2185collective CREDITS: Editing: Amanda Harvey-Sánchez and Stefan Hegerat; Original Music: Stefan Hegerat; Hosts: Amanda Harvey-Sánchez and Julia DaSilva; Guests: Aniket, Evelyn, and Kristine; Singalong: Rebecca and participants at CJTO’s September 2022 Orientation; Producer: Climate Justice Toronto
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.635 | 0.332 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".